I found the following definition from ML glossary here intuitive:

Tower - A component of a deep neural network that is itself a deep neural network without an output layer.


According to tensorflow documentation about CNN,

The first abstraction we require is a function for computing inference and gradients for a single model replica. In the code we term this abstraction a "tower".

To get the relevant context and more, check this.

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